semantic-scholar — agentic-workflow semantic-scholar, community, agentic-workflow, ide skills, knowledge-base, Claude Code, Cursor, Windsurf

v1.0.0

关于此技能

Try to make an agent-assisted knowledge base template

# 核心主题

duskmoon314 duskmoon314
[2]
[0]
更新于: 3/22/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 1/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Review Score
1/11
Quality Score
28
Canonical Locale
en
Detected Body Locale
en

Try to make an agent-assisted knowledge base template

核心价值

Try to make an agent-assisted knowledge base template

适用 Agent 类型

Suitable for operator workflows that need explicit guardrails before installation and execution.

赋予的主要能力 · semantic-scholar

! 使用限制与门槛

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The page lacks a strong recommendation layer.
  • - The page lacks concrete use-case guidance.
  • - The page lacks explicit limitations or caution signals.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

评审后的下一步

先决定动作,再继续看上游仓库材料

Killer-Skills 的主价值不应该停在“帮你打开仓库说明”,而是先帮你判断这项技能是否值得安装、是否应该回到可信集合复核,以及是否已经进入工作流落地阶段。

实验室 Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

常见问题与安装步骤

以下问题与步骤与页面结构化数据保持一致,便于搜索引擎理解页面内容。

? FAQ

semantic-scholar 是什么?

Try to make an agent-assisted knowledge base template

如何安装 semantic-scholar?

运行命令:npx killer-skills add duskmoon314/keine。支持 Cursor、Windsurf、VS Code、Claude Code 等 19+ IDE/Agent。

semantic-scholar 支持哪些 IDE 或 Agent?

该技能兼容 Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer。可使用 Killer-Skills CLI 一条命令通用安装。

安装步骤

  1. 1. 打开终端

    在你的项目目录中打开终端或命令行。

  2. 2. 执行安装命令

    运行:npx killer-skills add duskmoon314/keine。CLI 会自动识别 IDE 或 AI Agent 并完成配置。

  3. 3. 开始使用技能

    semantic-scholar 已启用,可立即在当前项目中调用。

! 参考页模式

此页面仍可作为安装与查阅参考,但 Killer-Skills 不再把它视为主要可索引落地页。请优先阅读上方评审结论,再决定是否继续查看上游仓库说明。

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

semantic-scholar

安装 semantic-scholar,这是一款面向AI agent workflows and automation的 AI Agent Skill。支持 Claude Code、Cursor、Windsurf,一键安装。

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Semantic Scholar Paper Metadata Skill

Search and retrieve research paper metadata using the Semantic Scholar Academic Graph API.

Base URL: https://api.semanticscholar.org/graph/v1 Auth: No key required for basic use. For higher rate limits, get a free key at https://www.semanticscholar.org/product/api and pass it as x-api-key header.


Choosing the Right Endpoint

GoalEndpoint
Search by keywords/topicGET /paper/search
Boolean/filtered bulk searchGET /paper/search/bulk
Match by exact/near-exact titleGET /paper/search/match
Look up a specific paper (DOI, arXiv, etc.)GET /paper/{paper_id}
Fetch multiple papers at oncePOST /paper/batch
Papers that cite a paperGET /paper/{paper_id}/citations
Papers referenced by a paperGET /paper/{paper_id}/references
Author's papersGET /author/{author_id}/papers

Field Selection

All endpoints accept a fields parameter — a comma-separated list. Default response only includes paperId and title. Always request the fields you need.

Common fields:

paperId, corpusId, externalIds, title, abstract, year, venue,
publicationDate, authors, citationCount, referenceCount,
influentialCitationCount, isOpenAccess, openAccessPdf,
fieldsOfStudy, s2FieldsOfStudy, publicationTypes

Nested fields use dot notation:

authors.name, authors.affiliations,
citations.title, citations.year, citations.authors,
references.title, references.externalIds,
openAccessPdf.url

Endpoint Details & curl Examples

1. Keyword Search — /paper/search

Best for: topic discovery, finding relevant papers by concept.

bash
1curl -G "https://api.semanticscholar.org/graph/v1/paper/search" \ 2 --data-urlencode "query=transformer attention mechanism" \ 3 --data-urlencode "fields=title,year,authors,citationCount,abstract" \ 4 --data-urlencode "limit=10"

Filters (all optional, combine freely):

bash
1--data-urlencode "year=2020-2024" # year range 2--data-urlencode "venue=NeurIPS,ICML" # specific venues 3--data-urlencode "fieldsOfStudy=Computer Science" 4--data-urlencode "minCitationCount=50" 5--data-urlencode "openAccessPdf=" # open access only (empty value = true) 6--data-urlencode "publicationDateOrYear=2023-01-01:2023-12-31"

Pagination: Response includes total, offset, next. Use offset=N to page through results (max 1,000 total).


2. Bulk Search — /paper/search/bulk

Best for: large result sets, boolean queries, sorted/filtered exports.

Supports AND/OR/NOT operators and returns up to 1,000 results per call with token-based pagination (no 1,000 total cap).

bash
1curl -G "https://api.semanticscholar.org/graph/v1/paper/search/bulk" \ 2 --data-urlencode "query=large language models AND safety" \ 3 --data-urlencode "fields=title,year,citationCount,authors" \ 4 --data-urlencode "sort=citationCount:desc" \ 5 --data-urlencode "limit=100"

Sort options: paperId, publicationDate, citationCount (append :asc or :desc).

Pagination: Response includes a token field. Pass it as &token=... on the next call.


3. Title Match — /paper/search/match

Best for: "I have this paper title, give me its metadata." Returns a single best match or 404. Includes a matchScore.

bash
1curl -G "https://api.semanticscholar.org/graph/v1/paper/search/match" \ 2 --data-urlencode "query=Attention Is All You Need" \ 3 --data-urlencode "fields=title,year,authors,citationCount,externalIds"

4. Paper by ID — /paper/{paper_id}

Best for: fetching full metadata when you already have an identifier.

Supported ID formats (prefix as shown):

FormatExample
S2 Paper ID (bare)649def34f8be52c8b66281af98ae884c09aef38a
Corpus IDCorpusId:215416146
DOIDOI:10.18653/v1/2020.acl-main.463
arXivARXIV:2005.14165
PubMedPMID:23193287
PubMed CentralPMCID:PMC4535869
ACL AnthologyACL:2020.acl-main.463
Semantic Scholar URLURL:https://www.semanticscholar.org/paper/...
bash
1# By arXiv ID 2curl "https://api.semanticscholar.org/graph/v1/paper/ARXIV:1706.03762?fields=title,abstract,year,authors,citationCount,referenceCount,isOpenAccess,openAccessPdf" 3 4# By DOI 5curl "https://api.semanticscholar.org/graph/v1/paper/DOI:10.1145/3292500.3330919?fields=title,year,venue,citationCount"

5. Batch Paper Lookup — POST /paper/batch

Best for: fetching metadata for a list of known paper IDs (up to 500).

bash
1curl -X POST "https://api.semanticscholar.org/graph/v1/paper/batch?fields=title,year,citationCount" \ 2 -H "Content-Type: application/json" \ 3 -d '{"ids": ["ARXIV:1706.03762", "ARXIV:2005.14165", "DOI:10.18653/v1/2020.acl-main.463"]}'

6. Citations & References

bash
1# Papers that CITE this paper (incoming) 2curl "https://api.semanticscholar.org/graph/v1/paper/ARXIV:1706.03762/citations?fields=title,year,authors,citationCount&limit=20" 3 4# Papers this paper CITES (outgoing) 5curl "https://api.semanticscholar.org/graph/v1/paper/ARXIV:1706.03762/references?fields=title,year,authors&limit=20"

Response wraps each item in a citingPaper or citedPaper key:

json
1{ "data": [{ "citingPaper": { "title": "...", "year": 2023 } }] }

7. Author Lookup

bash
1# Find an author's ID by name 2curl -G "https://api.semanticscholar.org/graph/v1/author/search" \ 3 --data-urlencode "query=Yoshua Bengio" \ 4 --data-urlencode "fields=name,affiliations,paperCount,citationCount,hIndex" 5 6# Get their papers 7curl "https://api.semanticscholar.org/graph/v1/author/{authorId}/papers?fields=title,year,citationCount&limit=20"

Rate Limits & API Key

  • Without a key: ~100 requests/5 min (shared pool)
  • With a free API key: ~1 request/sec sustained

To use an API key:

bash
1curl -H "x-api-key: YOUR_KEY" "https://api.semanticscholar.org/graph/v1/paper/..."

Store the key in an env var: export S2_API_KEY=... Then use: -H "x-api-key: $S2_API_KEY"


Parsing Results

Use jq to extract fields from responses:

bash
1# List titles and citation counts from a search 2curl -s -G "https://api.semanticscholar.org/graph/v1/paper/search" \ 3 --data-urlencode "query=BERT language model" \ 4 --data-urlencode "fields=title,year,citationCount" \ 5 --data-urlencode "limit=5" \ 6| jq '.data[] | "\(.citationCount)\t\(.year)\t\(.title)"' 7 8# Get all author names from a paper 9curl -s "https://api.semanticscholar.org/graph/v1/paper/ARXIV:1706.03762?fields=authors.name" \ 10| jq '[.authors[].name]' 11 12# Extract open access PDF URL 13curl -s "https://api.semanticscholar.org/graph/v1/paper/ARXIV:2005.14165?fields=openAccessPdf" \ 14| jq '.openAccessPdf.url'

Workflow Tips

  • Start with /paper/search/match when the user provides a full title — it's faster than a keyword search.
  • Use /paper/{id} when they give you a DOI, arXiv link, or PubMed ID — extract the ID and call directly.
  • For "papers about X" questions, use /paper/search with minCitationCount and year filters to surface foundational work.
  • For citation counts and h-index exploration, combine /author/search/author/{id}/papers sorted by citationCount:desc.
  • Always request only the fields you need — smaller responses, faster calls.
  • When displaying results, format as a table with title, year, venue, and citation count for readability.

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